Multi-object tracking (MOT) is one of the most important tasks of the vision perception
module of autonomous driving, which provides crucial inputs for the downstream task …

Multiple pedestrian tracking faces the challenge of tracking pedestrians in the presence of
occlusion. Existing methods suffer from inaccurate motion estimation, appearance feature …

Aiming at the problems of frequent identity switches (IDs) and trajectory interruption of multi-
pedestrian tracking algorithms in dense scenes, this paper proposes a multi-pedestrian …

Abstract Multi-Object Tracking (MOT) is a challenging research area in computer vision with
significant practical applications. With the advent of deep neural networks, significant …

This paper presents a study of two tracking algorithms (SORT~\cite {7533003} and
Tracktor++~\cite {2019}) that were ranked first positions on the MOT Challenge leaderboard …

Public security monitoring is a hot issue that the government and citizens pay close attention
to. Multiobject tracking plays an important role in solving many problems for public security …

Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different
road users that move across the field of view with different directions and speeds and that …

In this paper, we introduce a multi-pedestrian tracking algorithm for tracking from a moving
vehicle. The method is based on online learning of a random ferns (RF) tracker model using …

In this paper, we use the deep sort multi-target tracking algorithm to achieve multi-target
tracking of pedestrians and vehicles in traffic scenes. In this paper, firstly, yolov4 is used to …

Multi-pedestrian trackers perform well when targets are clearly visible making the
association task quite easy. However, when heavy occlusions are present, a mechanism to …